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function main_v57

Maturity: 37

Entry point function that instantiates a FixedProjectVictoriaGenerator and executes its complete pipeline to generate fixed disclosure documents.

File:
/tf/active/vicechatdev/fixed_project_victoria_generator.py
Lines:
1624 - 1628
Complexity:
simple

Purpose

This function serves as the main entry point for running the fixed disclosure generation pipeline. It creates an instance of FixedProjectVictoriaGenerator, executes the complete pipeline workflow, and returns the path to the generated output. This is typically used as the primary execution function for the disclosure generation system, orchestrating the entire process from initialization to completion.

Source Code

def main():
    """Main function to run the fixed disclosure generator."""
    generator = FixedProjectVictoriaGenerator()
    output_path = generator.run_complete_pipeline()
    return output_path

Return Value

Returns a string representing the file path to the generated output from the disclosure generator pipeline. The exact format and location of this path depends on the FixedProjectVictoriaGenerator's run_complete_pipeline() method implementation. This could be a local file path, a relative path, or an absolute path to the generated disclosure document.

Dependencies

  • os
  • re
  • json
  • tiktoken
  • typing
  • datetime
  • chromadb
  • langchain_openai
  • sentence_transformers
  • fitz
  • OneCo_hybrid_RAG

Required Imports

import os
import re
import json
import tiktoken
from typing import List, Dict, Any, Tuple
from datetime import datetime
import chromadb
from langchain_openai import ChatOpenAI
from sentence_transformers import CrossEncoder
import fitz
from OneCo_hybrid_RAG import MyEmbeddingFunction

Usage Example

# Ensure all environment variables are set
import os
os.environ['OPENAI_API_KEY'] = 'your-api-key-here'

# Import and run the main function
from your_module import main

# Execute the disclosure generation pipeline
output_path = main()
print(f'Disclosure generated at: {output_path}')

Best Practices

  • Ensure all required environment variables (especially OPENAI_API_KEY) are set before calling this function
  • Verify that the FixedProjectVictoriaGenerator class is properly defined and all its dependencies are satisfied
  • Handle the returned output_path appropriately, checking if the file exists and is valid
  • Consider wrapping this function call in try-except blocks to handle potential errors from the pipeline execution
  • This function has no parameters, so all configuration must be done through the FixedProjectVictoriaGenerator class initialization or environment variables
  • Ensure sufficient system resources (memory, disk space) are available as disclosure generation may be resource-intensive
  • Check that ChromaDB is properly initialized and accessible before running
  • Verify that all required PDF source documents are available if the pipeline processes PDFs

Tags

entry-point pipeline disclosure-generation orchestration main-function document-generation RAG LLM chromadb openai

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